Initial Questions to Explore

I am also curious to know if the number of hunters has changed from year to year.

__NOTICE__ that I am only looking at the general rifle hunting seasons on public land. There are also hunters for Archery, Muzzleloader, Private Land, Ranching for Wildlife, etc.

Setup

setwd("~/_code/colorado-dow/Phase I - Descriptive Analytics")

Load required libraries for wrangling data, charting, and mapping

library(plyr,quietly = T) # data wrangling
library(dplyr,quietly = T) # data wrangling
library(ggplot2, quietly = T) # charting
library(ggthemes,quietly = T) # so I can add the highcharts theme and palette
library(scales,quietly = T) # to load the percent function when labeling plots

Set our preferred charting theme

theme_set(theme_minimal()+theme_hc()+theme(legend.key.width = unit(1.5, "cm")))

Run script to get hunter data

source('~/_code/colorado-dow/datasets/Colorado Elk Harvest Data.R', echo=F)

Table of the hunter data

COElkRifleAll
source('~/_code/colorado-dow/datasets/Colorado GMUnit and Road data.R', echo=F)
## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/psarnow/_code/colorado-dow/datasets/CPW_GMUBoundaries/BigGameGMUBoundaries03172015.shp", layer: "BigGameGMUBoundaries03172015"
## with 185 features
## It has 12 fields
## Integer64 fields read as strings:  GMUID 
## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/psarnow/_code/colorado-dow/datasets/ne_10m_roads/ne_10m_roads.shp", layer: "ne_10m_roads"
## with 56601 features
## It has 29 fields
## Integer64 fields read as strings:  scalerank question

Take a peak at the boundary data

head(Unitboundaries2)

Total Elk Harvest

Statewide

# Group seasons
COElkHuntersStatewide <- summarise(group_by(COElkRifleAll,Year,Unit),
                                   Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T))
# Group Units
COElkHuntersStatewide <- summarise(group_by(COElkHuntersStatewide,Year),
                                   Hunters = sum(Hunters))

ggplot(COElkHuntersStatewide, aes(Year,Hunters)) +
  geom_bar(stat="identity",fill=ggthemes_data$hc$palettes$default[2]) +
  coord_cartesian(ylim = c(120000,160000)) +
  labs(title="Statewide Elk Hunters", caption="source: cpw.state.co.us")

At a highpoint in 2006 of ~151000, the number of hunters decreased to a low in 2009 of ~133000. Since 2009 the total number of hunters has slightly increased from year to year.

Hunters by Unit

I’d like to know where the hunters are distributed across the state.

# Group seasons
COElkUnitHunters <- summarise(group_by(COElkRifleAll,Year,Unit),
                              Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T))

Last year’s data

Year2017 <- filter(COElkUnitHunters, Year == "2017")
HunterstoPlot <- left_join(Unitboundaries2,Year2017, by=c("Unit"))
ggplot(HunterstoPlot, aes(long, lat, group = group)) + 
  geom_polygon(aes(fill = Hunters),colour = "grey50", size = .2) + #Unit boundaries
  geom_path(data = COroads,aes(x = long, y = lat, group = group), color="#3878C7",size=2) + #Roads
  geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=3) + #Unit labels
  scale_fill_distiller(palette = "Oranges",direction = 1,na.value = 'light grey') +
  xlab("") + ylab("") +
  labs(title="2017 Colorado Elk Hunters", caption="source: cpw.state.co.us")

TODO - commentary

Number of Hunters Rank of the Units

Would also be beneficial to rank each unit so I can reference later. In this case average the number of hunters of the last few years

HunterRanklast3 <- filter(COElkUnitHunters, as.numeric(Year) >= 2015)
HunterRanklast3 <- summarise(group_by(HunterRanklast3,Unit),
                                 Hunters = mean(Hunters,na.rm = T))
HunterRanklast3$HuntersRank = rank(-HunterRanklast3$Hunters)

HunterRanklast3 <- filter(HunterRanklast3, HuntersRank <= 50) # top 50 units

In order for the chart to retain the order of the rows, the X axis variable (i.e. the categories) has to be converted into a factor.

HunterRanklast3 <- HunterRanklast3[order(-HunterRanklast3$Hunters), ]  # sort
HunterRanklast3$Unit <- factor(HunterRanklast3$Unit, levels = HunterRanklast3$Unit)  # to retain the order in plot.

Lollipop Chart

ggplot(HunterRanklast3, aes(x=Unit, y=Hunters)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=Unit, 
                   xend=Unit, 
                   y=0, 
                   yend=Hunters)) + 
  labs(title="Average Elk Hunters 2015-2017\nTop 50 Units", subtitle="Hunters by Unit", caption="source: cpw.state.co.us")

TODO - commentary

Conclusion

Its interesting that some of these units with high amount of hunters doesn’t seem to coincide with how many elk are in those units. The Northwestern herd seems to have the most amount of hunters, but our last report indicated that units with more elk in them were in the South and Southwest.

Lets investigate that further. Is there a correlation of number of hunters to number of elk in each unit? If not, I would certainly be interested in which units have the highest ratio of Elk to Hunters